For public health surveillance, is machine learning worth the effort? What methods are relevant? Do you need special hardware? This talk was motivated by these and other questions asked by ISDS members. It will focus on providing practical—and slightly opinionated—advice about how to determine whether machine learning could be a useful tool for your problem.

This presentation given August 3, 2017 describes work toward applying machine learning methods to CDC’s autism surveillance program. CDC’s population-based autism surveillance is labor-intensive and costly, as it requires clinicians to manually review children’s medical and educational records for descriptions of autism symptoms. Using the words in these records, our team is building algorithms to predict which children will meet the surveillance case definition for autism. This talk describes our early results, recent progress, and perspectives gained from working with textual data.

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This Knowledge Repository is made possible through the activities of the Centers for Disease Control and Prevention Cooperative Agreement/Grant #1 NU500E000098-01, National Surveillance Program Community of Practice (NSSP-CoP): Strengthening Health Surveillance Capabilities Nationwide, which is in the interest of public health.